1. Technical Field
This description pertains generally to quantitative phase imaging, and more particularly to phase imaging with partially coherent illumination.
2. Background Discussion
Quantitative phase imaging has applications in biology and surface metrology, since objects of interest often do not absorb light but cause measurable phase delays. Phase cannot be directly measured by a camera, and so phase objects are invisible, i.e. transparent, in an in-focus imaging system. Phase retrieval methods use a series of images taken with various complex transfer functions. Methods that use intensity images measured through focus are especially interesting because they have the advantage of a simple experimental setup and wide applicability. The stack of defocused intensity images can be obtained in an imaging system with an axial motion stage and microscope.
Traditional methods for phase imaging include phase contrast microscopy, differential interference contrast (DIC) microscopy, and digital holography microscopy. Phase contrast microscopy and DIC image phase non-quantitatively. Digital holography microscopy can recover phase quantitatively, but it needs to measure an interference hologram to recover phase and thus requires laser illumination and a reference beam, significantly complicating the experimental setup and making it difficult to incorporate into existing imaging systems.
The Kalman filter method is an improved alternative to the transport of intensity equation (TIE) method, which is not robust to noise in the measurement. Kalman filtering can provide the information theoretic near-optimal phase solution, even in severe noise. However, standard Kalman filtering has limitations in practical use because of its high computational complexity and storage requirement.
When doing phase imaging in a commercial microscope, the partially coherent illumination can cause blurring of the phase result if a coherent model is used. The effect of partially coherent illumination has been neglected in most previous phase recovery algorithms.